search-conference

name	search-conference
description	Use when tasks involve semantically matching a user's idea (query or example papers) to papers in a specific OpenReview venue. Uses `embed-papers` to crawl metadata, build/use embedding caches, run cosine-similarity search, then produces a short, grouped Markdown reading list with brief rationales.

When to use

• Need to find papers in an OpenReview venue that match one or more ideas/topics.

Dependencies

• embed-papers must be installed and available in PATH.
  • Check with: command -v embed-papers
  • Install with: pip install embed-papers
• OpenReview must be reachable.
• OPENAI_API_KEY is required to compute embeddings of the papers (cached) or the search intent (not cached).
  • Check with: printenv OPENAI_API_KEY
  • If missing and embeddings are required: stop and tell the user how to set it (immediate stop).

Inputs from user

• Venue (one of):
  • venue_id (preferred), e.g. ICLR.cc/2024/Conference
  • OR {conference, year} to derive venue_id as {CONF}.cc/{YEAR}/Conference, e.g. NeurIPS, 2025
• Search intent (one of):
  • query (string of ideas)
  • OR examples (list of {title, abstract} objects)
• Optional:
  • top_k (default: 100) for retrieval breadth

CLI contract (how to interpret tool output)

• Success envelope: { ok: true, schema_version: "1", command, data }
• Error envelope: { ok: false, schema_version: "1", command, error: { type, message } }
• Always parse stdout as JSON.
• Treat any ok=false as a terminal error unless the error section below says otherwise.

HARD CONSTRAINT (TOOLS):

• Do NOT call Read/Glob/Grep on any cache/embedding files or directories (e.g. anything under .cache/ or any path containing "cache", "embedding", "paper", "atlas").
• Treat caches as opaque implementation details. Never inspect them “just to check”.
• If you need cache status, ONLY use embed-papers warm-cache and rely on its JSON stdout.
• If a command outputs a cache path, DO NOT open it; proceed using the CLI utilities.

Pipeline

1. Resolve venue_id

   • If the user gave {conference, year}, build: {CONF}.cc/{YEAR}/Conference
   • If ambiguous, ask a single clarifying question (conference acronym + year).

2. Crawl venue metadata (idempotent)

   • Run:
     • embed-papers crawl --venue-id "<venue_id>" --skip-if-exists
   • Record:
     • data.output_file
     • data.total

3. Ensure embeddings are available (cache)

   • Run:
     • embed-papers warm-cache --venue-id "<venue_id>"
   • If this fails due to missing API key, stop and instruct the user to set OPENAI_API_KEY.
   • This command also computes the embedding if no cache is found.
   • You MUST NOT access the cache.
   • You MUST use the package's provided utility.

4. Search (choose based on user input)

   • Query mode:
     • embed-papers search --venue-id "<venue_id>" --query "<query>" --top-k <top_k>
   • Examples mode:
     • If needed, write a temporary JSON file containing:
       • [{"title":"...","abstract":"..."}, ...]
     • Then run:
       • embed-papers search --venue-id "<venue_id>" --examples-file "<tmp.json>" --top-k <top_k>

5. Organize results (post-processing)

   • Group primarily by primary_area (if present).
   • Within groups, prefer papers with clear overlap to the query/examples.
   • For each recommended paper, add agent judgment notes:
     • why it matches
     • what seems novel/different
     • caveats (weak match, missing abstract, unclear claims, etc.)

Report requirements (Markdown only)

• Output is a Markdown report only (no raw JSON).
• Keep the final recommendation list short: 5-10 papers max.
• Do not output a full ranked list or appendix by default (only if the user asks).

What I'd start with

• Begin this section with a short, casual sentence (lowercase is fine).
  • Example: "here's what i recommend you to read as a beginning."
• Then list 5-10 papers.
• Each item must include:
  • Title (bold)
  • OpenReview link
  • 1-2 sentence rationale (fit + why it matters, use italic to emphasis)

How I organized it

• Briefly explain grouping logic and where judgment calls were applied.
• Note missing metadata (e.g., missing abstracts) when relevant.

Why these stand out

• Use informal labels in the narrative (no formal rubric), e.g.:
  • "the obvious hits"
  • "the surprisingly relevant ones"
  • "the quirky but promising picks"

Error handling

• NoPapersFoundError
  • Likely invalid venue_id; suggest the pattern {CONF}.cc/{YEAR}/Conference and ask for the correct venue.
• CacheMissRequiresApiKeyError
  • Instruct the user to set OPENAI_API_KEY and retry.
• OpenReviewRequestError / EmbeddingRequestError
  • Suggest retrying, reducing load (smaller top_k), or trying later (rate limits / transient failures).